An efficient decision making method based on hyperbolic fuzzy environment with new score function and its application in determining crime prone zones

In contemporary decision-making frameworks, particularly within criminal investigations, there is a critical need for precise and adaptable methodologies. Traditional approaches, such as fuzzy sets and their extensions like q-rung orthopair fuzzy sets (q-ROFS), have demonstrated effectiveness but are limited by their inability to handle optimistic and pessimistic degrees independently. Hyperbolic fuzzy sets (HyFS) are used in this study because they offer greater flexibility and accuracy by allowing autonomous assignment of optimism and pessimism degrees, potentially overcoming these limitations. Motivated by this need, this research proposes a new score function based on hyperbolic fuzzy sets (HyFS), demonstrating its superiority over existing approaches. We develop a groundbreaking methodology leveraging hyperbolic fuzzy sets (HyFS)-based decision-making and validate its practicality by identifying high-risk crime zones in Dibrugarh city. The study highlights the relevance of our approach in real-world decision-making situations, with potential applications beyond predictive policing. By combining hyperbolic fuzzy sets (HyFS)-based score functions with our innovative approach, we offer promising advancements in decision-making processes across diverse domains, significantly improving accuracy, facilitating better resource allocation, and ultimately strengthening crime prevention strategies.